Defining the biological basis of radiomic phenotypes in lung cancer.
Defining the biological basis of radiomic phenotypes in lung cancer.
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DOI：
10.7554/elife.23421
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发表时间：
2017-07-21
期刊：
影响因子：
7.7
通讯作者：
Aerts HJ
中科院分区：
文献类型：
--
作者：
Grossmann P;Stringfield O;El-Hachem N;Bui MM;Rios Velazquez E;Parmar C;Leijenaar RT;Haibe-Kains B;Lambin P;Gillies RJ;Aerts HJ
医学影像可以无创性地显示人类癌症的特征。放射组学是一个新兴的领域，它将这些医学图像转化为定量数据，以实现肿瘤的表型分析。虽然放射组学与几个临床终点有关，但放射组学、临床因素和肿瘤生物学之间的复杂关系在很大程度上是未知的。为此，我们分析了两个独立的队列，分别包括262名北美和89名欧洲肺癌患者，并一致地确定了先前未描述的放射影像学特征、分子途径和临床因素之间的关联。特别是，我们发现了影像学特征、免疫反应、炎症和生存之间的关系，并通过免疫组织化学染色进一步验证了这一点。此外，许多影像学特征显示了对特定通路的预测价值；例如，肿瘤内异质性特征预测RNA聚合酶转录活性（AUC = 0.62, p=0.03），强度分散预测泛素连接酶的自降解途径（AUC = 0.69, p<10-4）。最后，我们观察到，当结合放射组学、遗传学和临床信息时，预后生物标志物的表现最高（CI = 0.73, p<10-9），表明这些数据的互补价值。总之，我们证明放射学方法允许对肿瘤的分子和临床特征进行无创评估，因此有可能通过系统地分析标准医疗图像来推进临床决策。DOI: http://dx.doi.org/10.7554/eLife.23421.001医学成像涵盖了广泛的用于观察身体内部的技术，包括x射线、核磁共振扫描和超声波。一种称为放射组学的过程使用计算机算法来处理这些技术收集的数据，以识别和精确测量人类专家无法量化的大量特征。通过这样做，放射组学可以自动测量肿瘤的放射学特征。例如，放射组学可以确定肿瘤的大小、形状和质地，以帮助诊断癌症并指导治疗。研究表明，放射组学可以预测癌症的某些临床特征，比如癌症在体内扩散的程度，对治疗有多大的反应，以及患者存活的可能性。然而，这些放射学特征尚未与驱动癌症发展和扩散的生物过程精确联系起来。癌症的发生是由于基因变化激活了细胞中的“分子途径”，引发了细胞分裂和炎症等过程。为了准确地找出特定肿瘤背后的变化，需要使用基因组学技术分析活检或手术中的肿瘤样本。将放射组学特征与肿瘤中活跃的分子过程联系起来可以产生进一步的信息，可以补充分子数据。所有癌症患者的图像都是常规收集的，但分子数据却不是。因此，在某些情况下，这些图像可以用来推断个体患者癌症的分子基础。Grossmann等人现在分析了从大约350名肺癌患者收集的放射组学、基因组学和临床数据。该分析揭示了通常由基因组学检测到的生物过程——特别是炎症反应——与放射组学特征之间的联系。此外，这些特征也可能与临床特征有关，如肿瘤类型和患者存活率。通过对手术获得的肿瘤组织进行免疫组化染色，进一步验证了这些结果。进一步的研究表明，某些放射组学特征可以预测癌症发展的关键分子通路的状态（如炎症反应）。此外，Grossmann等人发现，结合放射组学、基因组学和临床参数的数据预测癌症将如何发展，比这些参数单独预测的效果更好。这些结果证明了放射组学数据对基因组和临床数据的补充价值。有许多不同的算法可用于处理放射组学的图像。在放射组学可以用于临床评估患者肿瘤背后的生物学过程之前，需要确定一个特定的算法，然后在前瞻性临床试验中进行测试。DOI: http://dx.doi.org/10.7554/eLife.23421.002
Medical imaging can visualize characteristics of human cancer noninvasively. Radiomics is an emerging field that translates these medical images into quantitative data to enable phenotypic profiling of tumors. While radiomics has been associated with several clinical endpoints, the complex relationships of radiomics, clinical factors, and tumor biology are largely unknown. To this end, we analyzed two independent cohorts of respectively 262 North American and 89 European patients with lung cancer, and consistently identified previously undescribed associations between radiomic imaging features, molecular pathways, and clinical factors. In particular, we found a relationship between imaging features, immune response, inflammation, and survival, which was further validated by immunohistochemical staining. Moreover, a number of imaging features showed predictive value for specific pathways; for example, intra-tumor heterogeneity features predicted activity of RNA polymerase transcription (AUC = 0.62, p=0.03) and intensity dispersion was predictive of the autodegration pathway of a ubiquitin ligase (AUC = 0.69, p<10-4). Finally, we observed that prognostic biomarkers performed highest when combining radiomic, genetic, and clinical information (CI = 0.73, p<10-9) indicating complementary value of these data. In conclusion, we demonstrate that radiomic approaches permit noninvasive assessment of both molecular and clinical characteristics of tumors, and therefore have the potential to advance clinical decision-making by systematically analyzing standard-of-care medical images. DOI: http://dx.doi.org/10.7554/eLife.23421.001 Medical imaging covers a wide range of techniques that are used to look inside the body, including X-rays, MRI scans and ultrasound. A process called radiomics uses computer algorithms to process the data collected by these techniques to identify and precisely measure a large number of features that would not otherwise be quantifiable by human experts. By doing so, radiomics can automatically measure the radiographic characteristics of a tumor. For example, radiomics can establish the size, shape and texture of a tumor to help to diagnose cancer and guide its treatment. Research has suggested that radiomics can predict certain clinical characteristics of cancer, such as how far through the body the cancer has spread, how likely it is to respond to treatment, and how likely a patient is to survive. However, these radiomic characteristics have not yet been precisely linked to the biological processes that drive how cancer develops and spreads. Cancers develop as a result of genetic changes that activate “molecular pathways” in the cells and trigger processes such as cell division and inflammation. To work out exactly which changes are behind a particular tumor, a sample of the tumor from biopsy or surgery is analyzed using genomics techniques. Linking radiomics features to the molecular processes active in a tumor can generate further information that can complement the molecular data. Images are routinely collected on all cancer patients yet molecular data is not. Hence, in some cases, the images can be used to infer the molecular underpinnings of cancer in individual patients. Grossmann et al. have now analyzed radiomic, genomic and clinical data collected from approximately 350 patients with lung cancer. The analysis revealed links between biological processes normally detected by genomics – in particular, inflammatory responses – and radiomics features. Furthermore, these features could also be associated with clinical characteristics, such as tumor type and patient survival rates. These results were further validated by using a technique called immunohistochemical staining on tumor tissue obtained by surgery. Further investigation revealed that certain radiomics features can predict the state of molecular pathways that are key to cancer development (such as the inflammatory response). Furthermore, Grossmann et al. found that combining data from radiomics, genomics and clinical parameters predicts how the cancer will progress better than any of these parameters can predict on their own. These results demonstrate the complementary value of radiomic data to genomic and clinical data. There are many different algorithms that can be used to process images for radiomics. Before radiomics can be used clinically to assess the biological processes underlying the tumors of patients, a specific algorithm needs to be decided upon and then tested in prospective clinical trials. DOI: http://dx.doi.org/10.7554/eLife.23421.002